Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations21003
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory112.0 B

Variable types

TimeSeries9
DateTime1
Categorical3

Timeseries statistics

Number of series9
Time series length21003
Starting point2011-11-01 00:00:00
Ending point2014-03-31 22:00:00
Period1 hour and 28.22 seconds
2024-12-18T09:47:50.472245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:50.897235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Alerts

apparentTemperature is highly overall correlated with dewPoint and 3 other fieldsHigh correlation
dewPoint is highly overall correlated with apparentTemperature and 3 other fieldsHigh correlation
humidity is highly overall correlated with visibilityHigh correlation
icon is highly overall correlated with summaryHigh correlation
precipType is highly overall correlated with apparentTemperature and 2 other fieldsHigh correlation
summary is highly overall correlated with iconHigh correlation
temperature is highly overall correlated with apparentTemperature and 3 other fieldsHigh correlation
visibility is highly overall correlated with humidityHigh correlation
windChillEffect is highly overall correlated with apparentTemperature and 2 other fieldsHigh correlation
precipType is highly imbalanced (76.8%) Imbalance
visibility is non stationary Non stationary
windBearing is non stationary Non stationary
temperature is non stationary Non stationary
dewPoint is non stationary Non stationary
pressure is non stationary Non stationary
apparentTemperature is non stationary Non stationary
windSpeed is non stationary Non stationary
humidity is non stationary Non stationary
windChillEffect is non stationary Non stationary
visibility is seasonal Seasonal
windBearing is seasonal Seasonal
temperature is seasonal Seasonal
dewPoint is seasonal Seasonal
pressure is seasonal Seasonal
apparentTemperature is seasonal Seasonal
windSpeed is seasonal Seasonal
humidity is seasonal Seasonal
windChillEffect is seasonal Seasonal
time has unique values Unique
windChillEffect has 10520 (50.1%) zeros Zeros

Reproduction

Analysis started2024-12-18 06:47:04.944916
Analysis finished2024-12-18 06:47:50.150160
Duration45.21 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

visibility
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct952
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.14531
Minimum0.18
Maximum16.09
Zeros0
Zeros (%)0.0%
Memory size328.2 KiB
2024-12-18T09:47:51.185411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile4.04
Q110.09
median12.26
Q313.07
95-th percentile14.1
Maximum16.09
Range15.91
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation3.0978788
Coefficient of variation (CV)0.27795358
Kurtosis1.3160563
Mean11.14531
Median Absolute Deviation (MAD)1.08
Skewness-1.3982886
Sum234084.95
Variance9.5968533
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.063155267 × 10-23
2024-12-18T09:47:51.310587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:51.732054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:51.851024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
13.07 815
 
3.9%
12.13 557
 
2.7%
13.52 516
 
2.5%
12.26 508
 
2.4%
16.09 239
 
1.1%
13.5 181
 
0.9%
12.68 177
 
0.8%
12.81 172
 
0.8%
13.02 171
 
0.8%
12.84 168
 
0.8%
Other values (942) 17499
83.3%
ValueCountFrequency (%)
0.18 1
< 0.1%
0.26 1
< 0.1%
0.27 1
< 0.1%
0.31 1
< 0.1%
0.32 1
< 0.1%
0.37 1
< 0.1%
0.39 1
< 0.1%
0.45 2
< 0.1%
0.48 1
< 0.1%
0.5 1
< 0.1%
ValueCountFrequency (%)
16.09 239
1.1%
16.01 11
 
0.1%
15.96 1
 
< 0.1%
15.93 1
 
< 0.1%
15.92 1
 
< 0.1%
15.9 1
 
< 0.1%
15.88 2
 
< 0.1%
15.85 1
 
< 0.1%
15.84 3
 
< 0.1%
15.82 2
 
< 0.1%
2024-12-18T09:47:51.424438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

windBearing
Numeric time series

Non stationary  Seasonal 

Distinct360
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.86849
Minimum0
Maximum359
Zeros45
Zeros (%)0.2%
Memory size328.2 KiB
2024-12-18T09:47:52.072997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q1121.5
median217
Q3256
95-th percentile331
Maximum359
Range359
Interquartile range (IQR)134.5

Descriptive statistics

Standard deviation90.661391
Coefficient of variation (CV)0.46286868
Kurtosis-0.7329996
Mean195.86849
Median Absolute Deviation (MAD)53
Skewness-0.45117202
Sum4113826
Variance8219.4879
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.009080761 × 10-21
2024-12-18T09:47:52.194270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:52.470872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:52.587223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
239 166
 
0.8%
215 153
 
0.7%
253 153
 
0.7%
241 153
 
0.7%
248 153
 
0.7%
219 153
 
0.7%
212 151
 
0.7%
229 150
 
0.7%
242 148
 
0.7%
238 148
 
0.7%
Other values (350) 19475
92.7%
ValueCountFrequency (%)
0 45
0.2%
1 28
0.1%
2 25
0.1%
3 39
0.2%
4 28
0.1%
5 35
0.2%
6 34
0.2%
7 39
0.2%
8 20
0.1%
9 25
0.1%
ValueCountFrequency (%)
359 35
0.2%
358 43
0.2%
357 39
0.2%
356 41
0.2%
355 27
0.1%
354 33
0.2%
353 34
0.2%
352 36
0.2%
351 38
0.2%
350 35
0.2%
2024-12-18T09:47:52.298770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

temperature
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2670
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.340575
Minimum-5.16
Maximum26.06
Zeros1
Zeros (%)< 0.1%
Memory size328.2 KiB
2024-12-18T09:47:52.775127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-5.16
5-th percentile1.42
Q16.445
median9.87
Q314.19
95-th percentile20.12
Maximum26.06
Range31.22
Interquartile range (IQR)7.745

Descriptive statistics

Standard deviation5.5971968
Coefficient of variation (CV)0.54128489
Kurtosis-0.33990246
Mean10.340575
Median Absolute Deviation (MAD)3.79
Skewness0.23303731
Sum217183.09
Variance31.328612
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value5.127138118 × 10-5
2024-12-18T09:47:52.890821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:53.171301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:53.422647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
9.13 35
 
0.2%
7.54 31
 
0.1%
9.11 31
 
0.1%
8.07 30
 
0.1%
7.48 30
 
0.1%
9.08 30
 
0.1%
9.16 29
 
0.1%
10.27 29
 
0.1%
10.33 29
 
0.1%
9.62 29
 
0.1%
Other values (2660) 20700
98.6%
ValueCountFrequency (%)
-5.16 1
< 0.1%
-5.04 1
< 0.1%
-4.89 1
< 0.1%
-4.46 1
< 0.1%
-4.43 1
< 0.1%
-4.17 1
< 0.1%
-4.11 1
< 0.1%
-3.99 1
< 0.1%
-3.87 1
< 0.1%
-3.86 1
< 0.1%
ValueCountFrequency (%)
26.06 3
< 0.1%
26.04 1
 
< 0.1%
26.03 2
< 0.1%
26.02 1
 
< 0.1%
26.01 1
 
< 0.1%
25.99 1
 
< 0.1%
25.98 1
 
< 0.1%
25.92 2
< 0.1%
25.9 2
< 0.1%
25.89 1
 
< 0.1%
2024-12-18T09:47:53.000294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

time
Date

Unique 

Distinct21003
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size328.2 KiB
Minimum2011-11-01 00:00:00
Maximum2014-03-31 22:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-18T09:47:53.548818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:53.625395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

dewPoint
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2375
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4756149
Minimum-9.98
Maximum18.88
Zeros4
Zeros (%)< 0.1%
Memory size328.2 KiB
2024-12-18T09:47:53.756548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.98
5-th percentile-2.01
Q12.81
median6.53
Q310.26
95-th percentile14.52
Maximum18.88
Range28.86
Interquartile range (IQR)7.45

Descriptive statistics

Standard deviation5.0098099
Coefficient of variation (CV)0.77364234
Kurtosis-0.47661831
Mean6.4756149
Median Absolute Deviation (MAD)3.73
Skewness-0.1234626
Sum136007.34
Variance25.098195
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.732825598 × 10-9
2024-12-18T09:47:53.872958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:54.149537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:54.259496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
8.11 36
 
0.2%
7.64 35
 
0.2%
8.21 33
 
0.2%
8.13 33
 
0.2%
7.09 33
 
0.2%
8.07 33
 
0.2%
5.46 32
 
0.2%
7.56 30
 
0.1%
8.18 30
 
0.1%
8.16 30
 
0.1%
Other values (2365) 20678
98.5%
ValueCountFrequency (%)
-9.98 1
< 0.1%
-9.68 1
< 0.1%
-9.54 1
< 0.1%
-9.42 1
< 0.1%
-9.33 1
< 0.1%
-8.92 1
< 0.1%
-8.89 1
< 0.1%
-8.67 1
< 0.1%
-8.44 1
< 0.1%
-8.37 1
< 0.1%
ValueCountFrequency (%)
18.88 1
< 0.1%
18.78 2
< 0.1%
18.67 1
< 0.1%
18.66 1
< 0.1%
18.65 1
< 0.1%
18.55 1
< 0.1%
18.52 1
< 0.1%
18.48 1
< 0.1%
18.46 1
< 0.1%
18.44 1
< 0.1%
2024-12-18T09:47:53.976093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

pressure
Numeric time series

Non stationary  Seasonal 

Distinct4987
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1014.0993
Minimum975.74
Maximum1043.32
Zeros0
Zeros (%)0.0%
Memory size328.2 KiB
2024-12-18T09:47:54.442882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum975.74
5-th percentile993.371
Q11007.395
median1014.77
Q31022
95-th percentile1032.409
Maximum1043.32
Range67.58
Interquartile range (IQR)14.605

Descriptive statistics

Standard deviation11.407181
Coefficient of variation (CV)0.011248584
Kurtosis0.16089281
Mean1014.0993
Median Absolute Deviation (MAD)7.3
Skewness-0.39378573
Sum21299127
Variance130.12378
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.739354251 × 10-17
2024-12-18T09:47:54.557467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:54.836676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:54.946789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1018.15 32
 
0.2%
1014.78 24
 
0.1%
1017.77 19
 
0.1%
1010.34 18
 
0.1%
1017.99 17
 
0.1%
1016.08 17
 
0.1%
1017.87 17
 
0.1%
1013.38 16
 
0.1%
1014.12 16
 
0.1%
1018.07 16
 
0.1%
Other values (4977) 20811
99.1%
ValueCountFrequency (%)
975.74 1
< 0.1%
975.87 1
< 0.1%
976 1
< 0.1%
976.1 1
< 0.1%
976.12 1
< 0.1%
976.15 2
< 0.1%
976.21 1
< 0.1%
976.22 1
< 0.1%
976.25 1
< 0.1%
976.26 1
< 0.1%
ValueCountFrequency (%)
1043.32 1
< 0.1%
1043.21 1
< 0.1%
1043.2 1
< 0.1%
1042.85 1
< 0.1%
1042.74 1
< 0.1%
1042.63 1
< 0.1%
1042.55 1
< 0.1%
1042.45 1
< 0.1%
1042.04 1
< 0.1%
1041.95 1
< 0.1%
2024-12-18T09:47:54.666915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

apparentTemperature
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct2986
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.088136
Minimum-8.88
Maximum26.19
Zeros4
Zeros (%)< 0.1%
Memory size328.2 KiB
2024-12-18T09:47:55.270624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-8.88
5-th percentile-1.72
Q13.87
median9
Q314.19
95-th percentile20.139
Maximum26.19
Range35.07
Interquartile range (IQR)10.32

Descriptive statistics

Standard deviation6.7663379
Coefficient of variation (CV)0.74452428
Kurtosis-0.72514222
Mean9.088136
Median Absolute Deviation (MAD)5.16
Skewness0.071294057
Sum190878.12
Variance45.783329
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.135756698 × 10-5
2024-12-18T09:47:55.387701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:55.667188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:55.779068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
10.27 29
 
0.1%
10.33 29
 
0.1%
10.31 27
 
0.1%
10.87 27
 
0.1%
4.56 27
 
0.1%
12.51 27
 
0.1%
11.29 25
 
0.1%
10.21 25
 
0.1%
11.41 25
 
0.1%
10.42 24
 
0.1%
Other values (2976) 20738
98.7%
ValueCountFrequency (%)
-8.88 1
< 0.1%
-8.57 1
< 0.1%
-8.52 1
< 0.1%
-8.41 1
< 0.1%
-8.36 1
< 0.1%
-8.35 1
< 0.1%
-8.34 1
< 0.1%
-8.33 1
< 0.1%
-8.26 1
< 0.1%
-7.99 1
< 0.1%
ValueCountFrequency (%)
26.19 2
< 0.1%
26.17 1
< 0.1%
26.07 2
< 0.1%
26.06 2
< 0.1%
26.04 1
< 0.1%
26.03 1
< 0.1%
26.02 1
< 0.1%
25.99 1
< 0.1%
25.98 1
< 0.1%
25.93 1
< 0.1%
2024-12-18T09:47:55.493178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

windSpeed
Numeric time series

Non stationary  Seasonal 

Distinct1095
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9121764
Minimum0.04
Maximum14.8
Zeros0
Zeros (%)0.0%
Memory size328.2 KiB
2024-12-18T09:47:55.975256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile1
Q12.43
median3.69
Q35.08
95-th percentile7.67
Maximum14.8
Range14.76
Interquartile range (IQR)2.65

Descriptive statistics

Standard deviation2.0279618
Coefficient of variation (CV)0.51837178
Kurtosis0.75681508
Mean3.9121764
Median Absolute Deviation (MAD)1.32
Skewness0.73568459
Sum82167.44
Variance4.1126292
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.588978116 × 10-24
2024-12-18T09:47:56.097074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:56.373270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:56.486732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3.25 77
 
0.4%
3.63 76
 
0.4%
2.83 75
 
0.4%
3.5 73
 
0.3%
3.71 71
 
0.3%
3.67 69
 
0.3%
3.59 65
 
0.3%
3.21 65
 
0.3%
3.84 65
 
0.3%
4.01 64
 
0.3%
Other values (1085) 20303
96.7%
ValueCountFrequency (%)
0.04 1
 
< 0.1%
0.06 1
 
< 0.1%
0.08 2
 
< 0.1%
0.1 2
 
< 0.1%
0.11 2
 
< 0.1%
0.12 2
 
< 0.1%
0.13 2
 
< 0.1%
0.14 5
< 0.1%
0.15 3
< 0.1%
0.16 4
< 0.1%
ValueCountFrequency (%)
14.8 1
< 0.1%
14.7 1
< 0.1%
14.56 1
< 0.1%
14.53 1
< 0.1%
14.11 1
< 0.1%
13.52 1
< 0.1%
13.48 1
< 0.1%
13.42 1
< 0.1%
13.38 1
< 0.1%
13.33 1
< 0.1%
2024-12-18T09:47:56.199930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

precipType
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size328.2 KiB
Rain
20210 
Snow
 
793

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters84012
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRain
2nd rowRain
3rd rowRain
4th rowRain
5th rowRain

Common Values

ValueCountFrequency (%)
Rain 20210
96.2%
Snow 793
 
3.8%

Length

2024-12-18T09:47:56.608039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T09:47:56.660967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
rain 20210
96.2%
snow 793
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n 21003
25.0%
R 20210
24.1%
a 20210
24.1%
i 20210
24.1%
S 793
 
0.9%
o 793
 
0.9%
w 793
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 21003
25.0%
R 20210
24.1%
a 20210
24.1%
i 20210
24.1%
S 793
 
0.9%
o 793
 
0.9%
w 793
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 21003
25.0%
R 20210
24.1%
a 20210
24.1%
i 20210
24.1%
S 793
 
0.9%
o 793
 
0.9%
w 793
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 21003
25.0%
R 20210
24.1%
a 20210
24.1%
i 20210
24.1%
S 793
 
0.9%
o 793
 
0.9%
w 793
 
0.9%

icon
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size328.2 KiB
Partly-Cloudy-Day
7534 
Partly-Cloudy-Night
5073 
Clear-Night
4659 
Clear-Day
1076 
Wind
989 
Other values (2)
1672 

Length

Max length19
Median length17
Mean length14.155454
Min length3

Characters and Unicode

Total characters297307
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear-Night
2nd rowPartly-Cloudy-Night
3rd rowClear-Night
4th rowPartly-Cloudy-Night
5th rowPartly-Cloudy-Night

Common Values

ValueCountFrequency (%)
Partly-Cloudy-Day 7534
35.9%
Partly-Cloudy-Night 5073
24.2%
Clear-Night 4659
22.2%
Clear-Day 1076
 
5.1%
Wind 989
 
4.7%
Cloudy 979
 
4.7%
Fog 693
 
3.3%

Length

2024-12-18T09:47:56.718255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T09:47:56.778388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
partly-cloudy-day 7534
35.9%
partly-cloudy-night 5073
24.2%
clear-night 4659
22.2%
clear-day 1076
 
5.1%
wind 989
 
4.7%
cloudy 979
 
4.7%
fog 693
 
3.3%

Most occurring characters

ValueCountFrequency (%)
y 34803
11.7%
l 31928
10.7%
- 30949
10.4%
a 26952
 
9.1%
t 22339
 
7.5%
C 19321
 
6.5%
r 18342
 
6.2%
d 14575
 
4.9%
o 14279
 
4.8%
u 13586
 
4.6%
Other values (10) 70233
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y 34803
11.7%
l 31928
10.7%
- 30949
10.4%
a 26952
 
9.1%
t 22339
 
7.5%
C 19321
 
6.5%
r 18342
 
6.2%
d 14575
 
4.9%
o 14279
 
4.8%
u 13586
 
4.6%
Other values (10) 70233
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y 34803
11.7%
l 31928
10.7%
- 30949
10.4%
a 26952
 
9.1%
t 22339
 
7.5%
C 19321
 
6.5%
r 18342
 
6.2%
d 14575
 
4.9%
o 14279
 
4.8%
u 13586
 
4.6%
Other values (10) 70233
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y 34803
11.7%
l 31928
10.7%
- 30949
10.4%
a 26952
 
9.1%
t 22339
 
7.5%
C 19321
 
6.5%
r 18342
 
6.2%
d 14575
 
4.9%
o 14279
 
4.8%
u 13586
 
4.6%
Other values (10) 70233
23.6%

humidity
Numeric time series

High correlation  Non stationary  Seasonal 

Distinct78
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78447127
Minimum0.23
Maximum1
Zeros0
Zeros (%)0.0%
Memory size328.2 KiB
2024-12-18T09:47:57.049951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.51
Q10.7
median0.81
Q30.89
95-th percentile0.96
Maximum1
Range0.77
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.13733204
Coefficient of variation (CV)0.17506319
Kurtosis0.17931688
Mean0.78447127
Median Absolute Deviation (MAD)0.09
Skewness-0.85558883
Sum16476.25
Variance0.01886009
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.299533957 × 10-18
2024-12-18T09:47:57.175598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:57.456638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:57.574155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.9 818
 
3.9%
0.87 793
 
3.8%
0.88 743
 
3.5%
0.93 738
 
3.5%
0.89 682
 
3.2%
0.86 678
 
3.2%
0.91 673
 
3.2%
0.85 671
 
3.2%
0.84 654
 
3.1%
0.96 653
 
3.1%
Other values (68) 13900
66.2%
ValueCountFrequency (%)
0.23 1
 
< 0.1%
0.24 1
 
< 0.1%
0.25 2
 
< 0.1%
0.26 4
< 0.1%
0.27 2
 
< 0.1%
0.28 1
 
< 0.1%
0.29 2
 
< 0.1%
0.3 6
< 0.1%
0.31 6
< 0.1%
0.32 5
< 0.1%
ValueCountFrequency (%)
1 30
 
0.1%
0.99 142
 
0.7%
0.98 77
 
0.4%
0.97 253
 
1.2%
0.96 653
3.1%
0.95 504
2.4%
0.94 579
2.8%
0.93 738
3.5%
0.92 575
2.7%
0.91 673
3.2%
2024-12-18T09:47:57.282260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

summary
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size328.2 KiB
Partly Cloudy
6548 
Mostly Cloudy
6059 
Clear
5735 
Overcast
979 
Foggy
693 
Other values (8)
989 

Length

Max length24
Median length13
Mean length10.629243
Min length5

Characters and Unicode

Total characters223246
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowPartly Cloudy
3rd rowClear
4th rowPartly Cloudy
5th rowPartly Cloudy

Common Values

ValueCountFrequency (%)
Partly Cloudy 6548
31.2%
Mostly Cloudy 6059
28.8%
Clear 5735
27.3%
Overcast 979
 
4.7%
Foggy 693
 
3.3%
Breezy And Mostly Cloudy 385
 
1.8%
Breezy And Partly Cloudy 267
 
1.3%
Breezy 195
 
0.9%
Breezy And Overcast 83
 
0.4%
Windy And Mostly Cloudy 29
 
0.1%
Other values (3) 30
 
0.1%

Length

2024-12-18T09:47:57.707585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cloudy 13291
37.1%
partly 6818
19.0%
mostly 6473
18.1%
clear 5735
16.0%
overcast 1071
 
3.0%
breezy 930
 
2.6%
and 776
 
2.2%
foggy 693
 
1.9%
windy 59
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 32317
14.5%
y 28264
12.7%
o 20457
9.2%
C 19026
8.5%
14843
 
6.6%
r 14554
 
6.5%
t 14362
 
6.4%
d 14126
 
6.3%
a 13624
 
6.1%
u 13291
 
6.0%
Other values (15) 38382
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 223246
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 32317
14.5%
y 28264
12.7%
o 20457
9.2%
C 19026
8.5%
14843
 
6.6%
r 14554
 
6.5%
t 14362
 
6.4%
d 14126
 
6.3%
a 13624
 
6.1%
u 13291
 
6.0%
Other values (15) 38382
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 223246
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 32317
14.5%
y 28264
12.7%
o 20457
9.2%
C 19026
8.5%
14843
 
6.6%
r 14554
 
6.5%
t 14362
 
6.4%
d 14126
 
6.3%
a 13624
 
6.1%
u 13291
 
6.0%
Other values (15) 38382
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 223246
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 32317
14.5%
y 28264
12.7%
o 20457
9.2%
C 19026
8.5%
14843
 
6.6%
r 14554
 
6.5%
t 14362
 
6.4%
d 14126
 
6.3%
a 13624
 
6.1%
u 13291
 
6.0%
Other values (15) 38382
17.2%

windChillEffect
Numeric time series

High correlation  Non stationary  Seasonal  Zeros 

Distinct1579
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2524387
Minimum-7
Maximum0.45
Zeros10520
Zeros (%)50.1%
Memory size328.2 KiB
2024-12-18T09:47:57.831120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-7
5-th percentile-4.12
Q1-2.57
median0
Q30
95-th percentile0
Maximum0.45
Range7.45
Interquartile range (IQR)2.57

Descriptive statistics

Standard deviation1.5560423
Coefficient of variation (CV)-1.24241
Kurtosis-0.64141627
Mean-1.2524387
Median Absolute Deviation (MAD)0
Skewness-0.79847266
Sum-26304.97
Variance2.4212677
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.887897053 × 10-13
2024-12-18T09:47:57.948842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-12-18T09:47:58.226522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Gap statistics

number of gaps26
min3 hours
max13 hours
mean7 hours, 11 minutes and 32.62 seconds
std2 hours, 53 minutes and 5.9 seconds
2024-12-18T09:47:58.339803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10520
50.1%
0.09 45
 
0.2%
0.04 43
 
0.2%
-2.47 39
 
0.2%
0.07 38
 
0.2%
0.05 36
 
0.2%
-2.97 35
 
0.2%
0.11 33
 
0.2%
-2.5 32
 
0.2%
0.02 32
 
0.2%
Other values (1569) 10150
48.3%
ValueCountFrequency (%)
-7 1
< 0.1%
-6.99 1
< 0.1%
-6.95 1
< 0.1%
-6.95 1
< 0.1%
-6.93 2
< 0.1%
-6.92 1
< 0.1%
-6.9 1
< 0.1%
-6.89 1
< 0.1%
-6.89 1
< 0.1%
-6.88 2
< 0.1%
ValueCountFrequency (%)
0.45 1
 
< 0.1%
0.45 1
 
< 0.1%
0.43 2
< 0.1%
0.42 1
 
< 0.1%
0.42 1
 
< 0.1%
0.41 2
< 0.1%
0.4 2
< 0.1%
0.39 2
< 0.1%
0.38 4
< 0.1%
0.36 1
 
< 0.1%
2024-12-18T09:47:58.055033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ACF and PACF

Interactions

2024-12-18T09:47:49.433864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.238974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.755970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.255483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.742352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.234997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.915982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.418904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.921736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.493783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.305409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.814273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.309889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.796204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.480566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.972284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.475072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.980651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.548846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.361648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.867442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.362939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.851880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.535981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.029293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.532650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.041145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.602472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.419721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.920734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.416708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.905070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.587041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.083404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.585985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.095328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.656640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.476419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.971973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.467961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.957451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.639638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.136391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.640137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.152757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.713214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.530290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.028997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.521502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.009151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.692522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.192094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.696194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.207648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.769274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.586769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.085435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.576610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.065974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.746943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.246694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.754431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.263732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.825261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.642373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.140752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.630343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.122313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.803090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.304368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.809224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.319654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.881339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:45.702063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.199317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:46.689029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.178494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:47.859963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.360801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:48.866050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-18T09:47:49.377256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-18T09:47:58.447444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
apparentTemperaturedewPointhumidityiconprecipTypepressuresummarytemperaturevisibilitywindBearingwindChillEffectwindSpeed
apparentTemperature1.0000.867-0.2720.1540.6950.0730.0900.9910.1300.0210.832-0.008
dewPoint0.8671.0000.1520.0820.605-0.0450.0600.864-0.074-0.0260.727-0.016
humidity-0.2720.1521.0000.2320.073-0.1610.165-0.303-0.568-0.079-0.040-0.256
icon0.1540.0820.2321.0000.0950.1220.8170.1520.4080.1190.1530.350
precipType0.6950.6050.0730.0951.0000.2660.0700.9950.1310.1640.3190.121
pressure0.073-0.045-0.1610.1220.2661.0000.1360.0400.0410.0470.147-0.332
summary0.0900.0600.1650.8170.0700.1361.0000.0810.3400.1020.1220.432
temperature0.9910.864-0.3030.1520.9950.0400.0811.0000.1590.0270.7730.081
visibility0.130-0.074-0.5680.4080.1310.0410.3400.1591.0000.208-0.0490.228
windBearing0.021-0.026-0.0790.1190.1640.0470.1020.0270.2081.0000.0080.042
windChillEffect0.8320.727-0.0400.1530.3190.1470.1220.773-0.0490.0081.000-0.272
windSpeed-0.008-0.016-0.2560.3500.121-0.3320.4320.0810.2280.042-0.2721.000

Missing values

2024-12-18T09:47:49.958889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-18T09:47:50.064393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

visibilitywindBearingtemperaturetimedewPointpressureapparentTemperaturewindSpeedprecipTypeiconhumiditysummarywindChillEffect
2011-11-01 00:00:0013.6316013.492011-11-01 00:00:0011.481008.1413.493.11RainClear-Night0.88Clear0.0
2011-11-01 01:00:0013.2615412.732011-11-01 01:00:0011.581007.8812.733.08RainPartly-Cloudy-Night0.93Partly Cloudy0.0
2011-11-01 02:00:0012.9416113.652011-11-01 02:00:0012.141007.0913.653.71RainClear-Night0.91Clear0.0
2011-11-01 03:00:0012.9917014.132011-11-01 03:00:0012.241006.5014.133.95RainPartly-Cloudy-Night0.88Partly Cloudy0.0
2011-11-01 04:00:0012.9218014.172011-11-01 04:00:0012.591006.1414.173.97RainPartly-Cloudy-Night0.90Partly Cloudy0.0
2011-11-01 05:00:0013.2918714.212011-11-01 05:00:0012.571006.3114.213.76RainPartly-Cloudy-Night0.90Partly Cloudy0.0
2011-11-01 06:00:0010.7022413.892011-11-01 06:00:0012.611006.6413.892.53RainPartly-Cloudy-Night0.92Mostly Cloudy0.0
2011-11-01 07:00:0011.0224513.022011-11-01 07:00:0011.011007.1513.022.49RainPartly-Cloudy-Day0.88Mostly Cloudy0.0
2011-11-01 08:00:0012.2124212.482011-11-01 08:00:0010.541008.0912.481.66RainPartly-Cloudy-Day0.88Partly Cloudy0.0
2011-11-01 09:00:0012.5924712.162011-11-01 09:00:0010.611008.8812.163.17RainPartly-Cloudy-Day0.90Mostly Cloudy0.0
visibilitywindBearingtemperaturetimedewPointpressureapparentTemperaturewindSpeedprecipTypeiconhumiditysummarywindChillEffect
2014-03-31 13:00:0015.5017716.372014-03-31 13:00:007.161012.8916.372.93RainClear-Day0.54Clear0.0
2014-03-31 14:00:0015.5017216.012014-03-31 14:00:007.431012.4216.012.59RainPartly-Cloudy-Day0.57Mostly Cloudy0.0
2014-03-31 15:00:0015.5014916.282014-03-31 15:00:007.461012.0916.282.67RainPartly-Cloudy-Day0.56Mostly Cloudy0.0
2014-03-31 16:00:0015.5514215.852014-03-31 16:00:007.861011.7415.852.87RainPartly-Cloudy-Day0.59Mostly Cloudy0.0
2014-03-31 17:00:0016.0913415.742014-03-31 17:00:007.931011.5215.742.29RainPartly-Cloudy-Day0.60Mostly Cloudy0.0
2014-03-31 18:00:0014.8112215.412014-03-31 18:00:007.281011.5915.411.98RainClear-Day0.58Clear0.0
2014-03-31 19:00:0014.1513014.622014-03-31 19:00:007.681011.8314.621.58RainPartly-Cloudy-Night0.63Partly Cloudy0.0
2014-03-31 20:00:0014.0714113.622014-03-31 20:00:007.911011.9813.621.14RainPartly-Cloudy-Night0.68Partly Cloudy0.0
2014-03-31 21:00:0012.9613513.032014-03-31 21:00:007.961011.9713.031.11RainClear-Night0.71Clear0.0
2014-03-31 22:00:0013.1311012.512014-03-31 22:00:007.971011.8312.510.94RainClear-Night0.74Clear0.0